Identification of significant genes with poor prognosis in ovarian cancer via bioinformatical analysis

Circ_0036490 and DKK1 competitively bind miR-29a to promote lipopolysaccharides-induced human gingival fibroblasts injury

MicroRNA (miRNA) plays a regulatory role in periodontitis. This study aimed to explore whether miR-29a could affect lipopolysaccharides (LPSs)-induced injury in human gingival fibroblasts (HGFs) through the competitive endogenous RNAs (ceRNA) mechanism. Periodontal ligament (PDL) tissues and HGFs were derived from patients with periodontitis and healthy volunteers. Periodontitis cell model was established by treating HGFs with LPS. Expression levels of circ_0036490, miR-29a, and DKK1 were evaluated by the reverse transcription quantitative real-time PCR (RT-qPCR) method. Western blotting assay was performed to assess protein expression levels of pyroptosis-related proteins and Wnt signalling related proteins. Cell viability was evaluated by cell counting kit-8 (CCK-8) assay. Concentration of lactate dehydrogenase (LDH), interleukin (IL)-1β, and IL-18 were determined by Enzyme-linked immunosorbent assay (ELISA). Pyroptosis rate were determined by flow cytometry assay to evaluate pyroptosis. The interaction between miR-29a and circ_0036490 or DKK1 was verified by dual-luciferase reporter and RNA pull-down assays. MiR-29a expression was lower in PDL tissues of patients with periodontitis than that in healthy group; likewise, miR-29a was also downregulated in LPS-treated HGFs. Overexpression of miR-29a increased cell viability and decreased pyroptosis of HGFs induced by LPS while inhibition of miR-29a exerted the opposite role. MiR-29a binds to circ_0036490 and elevation of circ_0036490 contributed to dysfuntion of LPS-treated HGFs and reversed the protection function of elevated miR-29a. In addition, miR-29a targets DKK1. Overexpression of DKK1 abrogated the effects of overexpressed miR-29a on cell vaibility, pyroptosis, and protein levels of Wnt signalling pathway of LPS-treated HGFs. Circ_0036490 and DKK1 competitively bind miR-29a to promote LPS-induced HGF injury in vitro. Wnt pathway inactivated by LPS was activated by miR-29a. Thence, miR-29a may be a promising target for periodontitis.

Systematic proteomics analysis revealed different expression of laminin interaction proteins in breast cancer: lower in luminal subtype and higher in claudin-low subtype

Background

Breast cancer is a major public health concern. Proteomics enables identification of proteins with aberrant properties. Here, we identified proteins with abnormal expression levels in breast cancer tissues and systematically analyzed and validated the data to locate potential diagnostic and therapeutic targets.

Methods

Protein expression level in breast cancer tissues and para-carcinoma tissues were detected by Isobaric Tags for Relative and Absolute Quantification (iTRAQ) technology and further screened through Gene Expression Profiling Interactive Analysis (GEPIA) database. Cellular components, protein domain and Reactome pathway analysis were performed to screen functional targets. Abnormal expression levels of functional targets were validated by Oncomine database, quantitative real time polymerase chain reaction (qRT-PCR) and proteomics detection. Protein correlation analysis was performed to explain the abnormal expression levels of potential targets in breast cancer.

Results

Overall, 207 and 207 proteins were up- and down-regulated, respectively, in breast cancer tissues, and approximately 50% were also detected in the GEPIA database. The overlapping proteins were mainly extracellular proteins containing epidermal growth factor-like domain in leukocyte adhesion molecule (EGF-Lam) domain and enriched in laminin interaction pathway. Moreover, the downregulated laminin interaction proteins could be functional targets, which were also validated through Oncomine-Richardson and Oncomine-Curtis database. However, the lower expression level of laminin interaction proteins only fit for luminal breast cancer cells with no or low metastasis ability because the proteins achieved higher expression level in more invasive claudin-low breast cancer cells. In addition, when compared with corresponding in situ carcinoma tissues, above-mentioned proteins also showed higher expression levels in invasive carcinoma tissues. Finally, we have revealed the negative correlation between the laminin interaction proteins and the claudins.

Conclusions

The laminin interaction protein, especially for laminins with β1 and γ1 subunits and their integrin receptors with α1 and α6 subunits, showed lower expression levels in luminal breast cancer with no or lower metastatic ability, but showed higher expression levels in claudin-low breast cancer with higher metastatic ability; and their higher expression could be related to the low claudin expression.

Boosting edgeR (Robust) by dealing with missing observations and gene-specific outliers in RNA-Seq profiles and its application to explore biomarker genes for diagnosis and therapies of ovarian cancer

The edgeR (Robust) is a popular approach for identifying differentially expressed genes (DEGs) from RNA-Seq profiles. However, it shows weak performance against gene-specific outliers and is unable to handle missing observations. To address these issues, we proposed a pre-processing approach of RNA-Seq count data by combining the iLOO-based outlier detection and random forest-based missing imputation approach for boosting the performance of edgeR (Robust). Both simulation and real RNA-Seq count data analysis results showed that the proposed edgeR (Robust) outperformed than the conventional edgeR (Robust). To investigate the effectiveness of identified DEGs for diagnosis, and therapies of ovarian cancer (OC), we selected top-ranked 12 DEGs (IL6, XCL1, CXCL8, C1QC, C1QB, SNAI2, TYROBP, COL1A2, SNAP25, NTS, CXCL2, and AGT) and suggested hub-DEGs guided top-ranked 10 candidate drug-molecules for the treatment against OC. Hence, our proposed procedure might be an effective computational tool for exploring potential DEGs from RNA-Seq profiles for diagnosis and therapies of any disease.

Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach

Target-driven cancer therapy is a notable advancement in precision oncology that has been accompanied by substantial medical accomplishments. Ovarian cancer is a highly frequent neoplasm in women and exhibits significant genomic and clinical heterogeneity. In a previous publication, we presented an extensive bioinformatics study aimed at identifying specific biomarkers associated with ovarian cancer. The findings of the network analysis indicate the presence of a cluster of nine dysregulated hub genes that exhibited significance in the underlying biological processes and contributed to the initiation of ovarian cancer. Here in this research article, we are proceeding our previous research by taking all hub genes into consideration for further analysis. GEPIA2 was used to identify patterns in the expression of critical genes. The KM plotter analysis indicated that the out of all genes 5 genes are statistically significant. The cBioPortal platform was further used to investigate the frequency of genetic mutations across the board and how they affected the survival of the patients. Maximum mutation was reported by ELAVL2. In order to discover viable therapeutic candidates after competitive inhibition of ELAVL2 with small molecular drug complex, high throughput screening and docking studies were used. Five compounds were identified. Overall, our results suggest that the ELAV-like protein 2-ZINC03830554 complex was relatively stable during the molecular dynamic simulation. The five compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that ELAVL2, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

NUSAP1 promotes pancreatic ductal adenocarcinoma progression by drives the epithelial-mesenchymal transition and reduces AMPK phosphorylation

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and its molecular mechanisms are unclear. Nucleolar and spindle-associated protein 1 (NUSAP1), an indispensable mitotic regulator, has been reported to be involved in the development of several types of tumors. The biological function and molecular mechanism of NUSAP1 in PDAC remain controversial. This study explored the effects and mechanism of NUSAP1 in PDAC.

METHODS

Differentially expressed genes (DEGs) were screened. A protein‒protein interaction (PPI) network was constructed to identify hub genes. Experimental studies and tissue microarray (TMA) analysis were performed to investigate the effects of NUSAP1 in PDAC and explore its mechanism.

RESULTS

Network analysis revealed that NUSAP1 is an essential hub gene in the PDAC transcriptome. Genome heterogeneity analysis revealed that NUSAP1 is related to tumor mutation burden (TMB), loss of heterozygosity (LOH) and homologous recombination deficiency (HRD) in PDAC. NUSAP1 is correlated with the levels of infiltrating immune cells, such as B cells and CD8 T cells. High NUSAP1 expression was found in PDAC tissues and was associated with a poor patient prognosis. NUSAP1 promoted cancer cell proliferation, migration and invasion, drives the epithelial-mesenchymal transition and reduces AMPK phosphorylation.

CONCLUSIONS

NUSAP1 is an essential hub gene that promotes PDAC progression and leads to a dismal prognosis by drives the epithelial-mesenchymal transition and reduces AMPK phosphorylation.

Identification of key genes and regulatory mechanisms in adult degenerative scoliosis

BACKGROUND

Adult degenerative scoliosis (ADS) is a spinal disorder, but its pathogenesis remain unclear. Therefore, in this study, we utilized data from the GEO database and explored the key genes and regulatory mechanisms involved in ADS.

METHODS

We performed bioinformatics analysis on the GSE209825 dataset of GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify ADS-related gene modules, and we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. We constructed a protein-protein interaction (PPI) network using the STRING database. We validated the specificity of hub genes in ADS using the GSE34095 dataset and plotted ROC curves for the identification of different degenerative spinal diseases based on the hub genes expression RESULTS: We identified 113 differentially expressed lncRNAs. WGCNA identified the MEblack module had the strongest correlation to ADS. GO and KEGG analyses of target genes in lncRNAs revealed their involvement in immune responses, inflammation, cellular processes, and metabolic pathways. Through PPI and ROC analysis, 10 hub genes linked to ADS diseases with certain specificity were found: ELANE, LTF, DEFA1B, SLC2A4, DEFA1, FAXDC2, LCN2, CTSB, FDFT1, and AURKA.

CONCLUSIONS

We identified 10 potential hub genes associated with ADS and constructed a transcription factors (TFs)-lncRNAs-hub genes regulatory network. These findings provide a new direction and research basis for the targeted treatment and mechanism research of ADS.

A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network

Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.

Antitumor effect of luteolin proven by patient-derived organoids of gastric cancer

Luteolin (Lut) has been shown to inhibit gastric cancer (GC); however, its efficacy compared to other clinical drugs has not been examined in human samples. This study aimed to elucidate the antitumor activity of Lut in GC patient-derived organoids (PDOs). PDOs were established from GC cancer tissues, and the characterization of tissues and PDOs was performed using whole-exome sequencing. Drug sensitivity tests were performed by treating PDOs with Lut, norcantharidin (NCTD), and carboplatin (CP). RNA sequencing of PDOs was performed to elucidate the antitumor mechanism of Lut, which was further verified in three GC cell lines. Eleven PDOs were successfully constructed, and were highly consistent with the pathophysiology and genetic changes in the corresponding tumors. The IC50s of Lut, NCTD, and CP of PDOs were 27.19, 23.9, and 37.87 μM, respectively. Lut treatment upregulated FOXO3, DUSP1, and CDKN1A expression and downregulated IL1R1 and FGFR4 expression in GC cell lines, which was consistent with the results of PDOs. We demonstrate that Lut exerted stronger antitumor effects than CP, but a similar effect to that of NCTD, which was obtained in an in vitro PDO system. Additionally, Lut exerted varying degrees of antitumor effects against the PDOs, thereby indicating that PDO may be a useful preclinical drug screening tool for personalized treatment.

Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis

Idiopathic pulmonary fibrosis (IPF) is the most common and serious type of idiopathic interstitial pneumonia, characterized by chronic, progressive, and low survival rates, while unknown disease etiology. Until recently, patients with idiopathic pulmonary fibrosis have a poor prognosis, high mortality, and limited treatment options, due to the lack of effective early diagnostic and prognostic tools. Therefore, we aimed to identify biomarkers for idiopathic pulmonary fibrosis based on multiple machine-learning approaches and to evaluate the role of immune infiltration in the disease. The gene expression profile and its corresponding clinical data of idiopathic pulmonary fibrosis patients were downloaded from Gene Expression Omnibus (GEO) database. Next, the differentially expressed genes (DEGs) with the threshold of FDR < 0.05 and |log2 foldchange (FC)| > 0.585 were analyzed via R package "DESeq2" and GO enrichment and KEGG pathways were run in R software. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms were combined to screen the key potential biomarkers of idiopathic pulmonary fibrosis. The diagnostic performance of these biomarkers was evaluated through receiver operating characteristic (ROC) curves. Moreover, the CIBERSORT algorithm was employed to assess the infiltration of immune cells and the relationship between the infiltrating immune cells and the biomarkers. Finally, we sought to understand the potential pathogenic role of the biomarker (SLAIN1) in idiopathic pulmonary fibrosis using a mouse model and cellular model. A total of 3658 differentially expressed genes of idiopathic pulmonary fibrosis were identified, including 2359 upregulated genes and 1299 downregulated genes. FHL2, HPCAL1, RNF182, and SLAIN1 were identified as biomarkers of idiopathic pulmonary fibrosis using LASSO logistic regression, RF, and SVM-RFE algorithms. The ROC curves confirmed the predictive accuracy of these biomarkers both in the training set and test set. Immune cell infiltration analysis suggested that patients with idiopathic pulmonary fibrosis had a higher level of B cells memory, Plasma cells, T cells CD8, T cells follicular helper, T cells regulatory (Tregs), Macrophages M0, and Mast cells resting compared with the control group. Correlation analysis demonstrated that FHL2 was significantly associated with the infiltrating immune cells. qPCR and western blotting analysis suggested that SLAIN1 might be a signature for the diagnosis of idiopathic pulmonary fibrosis. In this study, we identified four potential biomarkers (FHL2, HPCAL1, RNF182, and SLAIN1) and evaluated the potential pathogenic role of SLAIN1 in idiopathic pulmonary fibrosis. These findings may have great significance in guiding the understanding of disease mechanisms and potential therapeutic targets in idiopathic pulmonary fibrosis.

The potential bioactive ingredients and hub genes of five TCM prescriptions against lung adenocarcinoma were explored based on bioinformatics

Analysis of the commonness of several prescriptions of traditional Chinese medicine (TCM) in the treatment of lung adenocarcinoma (LUAD) based on bioinformatics. Searched the TCM prescriptions for the treatment of LUAD in the literature published in the database, searched ingredients in the TCM through TCMSP and Swiss target prediction databases (OB ≥ 30%, DL > 0.18, Caco-2 > 0), and predicted the potential targets. GEO database retrieved LUAD gene chip data and screened (P < 0.05, | log2 (fold change) |> 1). The biological function, hub gene selection and survival period, immune infiltration, methylation, copy number variations (CNVs), and single-nucleotide variants (SNV) of hub genes were analyzed by DAVID, STRING, Kaplan-Meier plotter database, Cytoscape software, GSCALite database, and TIMER2.0. In this study, 5 TCM prescriptions were analyzed, and a total of 173 ingredients were obtained through database search, including 35 coincidence ingredients, a total of 603 potential targets, 621 LUAD-related genes, 16 up-regulated genes, and 31 down-regulated genes. A total of 61 terms of biological process (BP), 14 terms of cellular component (CC), and 14 terms of molecular function (MF) were obtained. Twenty core genes were obtained, including 15 genes with different survival periods, which were closely related to immune cells (B cell, CD8 + T cell, CD4 + T cell, macrophage, neutrophil, and dendritic cells). The low expression of ADRB2 and MAOA and the high expression of AUARK, CDK1, KIF11, MIF, TOP2A, and TTK were associated with the survival rate of LUAD patients (P < 0.05). Baicalein, Arachidonate, Hederagenin, and hub genes may become potential drugs and potential targets for LUAD treatment. Evaluated the efficacy of TCM in the treatment of LUAD from macro to micro, mined the hub genes, and predicted the mechanism of action, so as to lay the foundation for the development of new drugs of TCM, prescription optimization, or disease control.

Prolonged fasting induces significant germ cell loss in chickens after hatching

Germ cell loss is a crucial biological event during germ cell development. The number of female germ cells determines the reproductive performance and egg production of hens. Various intrinsic and extrinsic factors affect germ cell loss, such as germ cell nest breakdown in early life and nutritional deficiencies during daily husbandry. Here, we examined the effect of fasting on the germ cell number of chicks. The results showed that 72 h fasting resulted in a higher germ cell loss than that by 24 h fasting in chicks. The RNA-seq analysis revealed that the genes of ribosome pathway were down-regulated and the biological processes of protein processing in endoplasmic reticulum were inhibited in starved chicks. Furthermore, in female chicks treated with 72 h fasting, the qPCR of ovaries showed down-regulation of ribosome-related genes, and transmission electron microscopy imaging of ovaries showed fewer ribosomes. The blood biochemical indices indicated that 72 h fasting reduced the liver functions and affected the glucose metabolism, lipid metabolites and ion metabolites. In summary, the present results concluded negative impacts on the germ cell pool by prolonged fasting in the early life of chicks and manifested that adequate management should be cared for fasted time for breeding.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

TNF-α/STAT1/CXCL10 mutual inflammatory axis that contributes to the pathogenesis of experimental models of multiple sclerosis: A promising signaling pathway for targeted therapies

BACKGROUND

Identifying mutual neuroinflammatory axis in different experimental models of multiple sclerosis (MS) is essential to evaluate the de- and re-myelination processes and improve therapeutic interventions' reproducibility.

METHODS

The expression profile data set of EAE (GSE47900) and cuprizone (GSE100663) models were downloaded from the Gene Expression Omnibus database. The R package and GEO2R software processed these raw chip data. Gene Ontology (GO) functional analysis, KEGG pathway analysis, and protein-protein interaction network analysis were performed to investigate interactions between common differentially expressed genes (DEGs) in all models. Finally, the ELISA method assessed the protein level of highlighted mutual cytokines in serum.

RESULTS

Our data introduced 59 upregulated [CXCL10, CCL12, and GBP6 as most important] and 17 downregulated [Serpinb1a, Prr18, and Ugt8a as most important] mutual genes. The signal transducer and activator of transcription 1 (STAT1) and CXCL10 were the most crucial hub proteins among mutual upregulated genes. These mutual genes were found to be mainly involved in the TNF-α, TLRs, and complement cascade signaling, and animal models shared 26 mutual genes with MS individuals. Finally, significant upregulation of serum level of TNF-α/IL-1β/CXCL10 cytokines was confirmed in all models in a relatively similar pattern.

CONCLUSION

For the first time, our study revealed the common neuroinflammatory pathway in animal models of MS and introduced candidate hub genes for better evaluating the preclinical efficacy of pharmacological interventions and designing prospective targeted therapies.

Identification of the Genetic Influence of SARS-CoV-2 Infections on IgA Nephropathy Based on Bioinformatics Method

INTRODUCTION

Coronavirus disease-2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. It was initially detected in Wuhan, China, in December 2019. In March 2020, the World Health Organization (WHO) declared COVID-19 a global pandemic. Compared to healthy individuals, patients with IgA nephropathy (IgAN) are at a higher risk of SARS-CoV-2 infection. However, the potential mechanisms remain unclear. This study explores the underlying molecular mechanisms and therapeutic agents for the management of IgAN and COVID-19 using the bioinformatics and system biology way.

METHODS

We first downloaded GSE73953 and GSE164805 from the Gene Expression Omnibus (GEO) database to obtain common differentially expressed genes (DEGs). Then, we performed the functional enrichment analysis, pathway analysis, protein-protein interaction (PPI) analysis, gene regulatory networks analysis, and potential drug analysis on these common DEGs.

RESULTS

We acquired 312 common DEGs from the IgAN and COVID-19 datasets and used various bioinformatics tools and statistical analyses to construct the PPI network to extract hub genes. Besides, we performed gene ontology (GO) and pathway analyses to reveal the common correlation between IgAN and COVID-19. Finally, on the basis of common DEGs, we determined the interactions between DEGs-miRNAs, the transcription factor-genes (TFs-genes), protein-drug, and gene-disease networks.

CONCLUSION

We successfully identified hub genes that may act as biomarkers of COVID-19 and IgAN and also screened out some potential drugs to provide new ideas for COVID-19 and IgAN treatment.

Comprehensively analyzing the genetic alterations, and identifying key genes in ovarian cancer

Though significant improvements have been made in the treatment methods for ovarian cancer (OC), the prognosis for OC patients is still poor. Exploring hub genes associated with the development of OC and utilizing them as appropriate potential biomarkers or therapeutic targets is highly valuable. In this study, the differentially expressed genes (DEGs) were identified from an independent GSE69428 Gene Expression Omnibus (GEO) dataset between OC and control samples. The DEGs were processed to construct the protein-protein interaction (PPI) network using STRING. Later, hub genes were identified through Cytohubba analysis of the Cytoscape. Expression and survival profiling of the hub genes were validated using GEPIA, OncoDB, and GENT2. For exploring promoter methylation levels and genetic alterations in hub genes, MEXPRESS and cBioPortal were utilized, respectively. Moreover, DAVID, HPA, TIMER, CancerSEA, ENCORI, DrugBank, and GSCAlite were used for gene enrichment analysis, subcellular localization analysis, immune cell infiltration analysis, exploring correlations between hub genes and different diverse states, lncRNA-miRNA-mRNA co-regulatory network analysis, predicting hub gene-associated drugs, and conducting drug sensitivity analysis, respectively. In total, 8947 DEGs were found between OC and normal samples in GSE69428. After STRING and Cytohubba analysis, 4 hub genes including TTK (TTK Protein Kinase), (BUB1 mitotic checkpoint serine/threonine kinase B) BUB1B, (Nucleolar and spindle-associated protein 1) NUSAP1, and (ZW10 interacting kinetochore protein) ZWINT were selected as the hub genes. Further, it was validated that these 4 hub genes were significantly up-regulated in OC samples compared to normal controls, but overexpression of these genes was not associated with overall survival (OS). However, genetic alterations in those genes were found to be linked with OS and disease-free (DFS) survival. Moreover, this study also revealed some novel links between TTK, BUB1B, NUSAP1, and ZWINT overexpression and promoter methylation status, immune cell infiltration, miRNAs, gene enrichment terms, and various chemotherapeutic drugs. Four hub genes, including TTK, BUB1B, NUSAP1, and ZWINT, were revealed as tumor-promotive factors in OC, having the potential to be utilized as novel biomarkers and therapeutic targets for OC management.

Discovery of pyrazolo[3,4-b]pyridine derivatives as novel and potent Mps1 inhibitors for the treatment of cancer

Monopolar spindle kinase 1 (Mps1) is a key element of the mitotic checkpoint and clinically evaluated as a target in the treatment of aggressive tumors. With this aim, a set of pyrazolo[3,4-b]pyridine-based compounds as new Mps1 inhibitors was investigated through a multidisciplinary approach, based on virtual screening, chemical synthesis and biological evaluation. One of the representative compounds, 31, exhibited strong kinase inhibitory potency against Mps1 with an IC₅₀ value of 2.596 nM and significantly inhibited proliferation of cancer cells, especially MDA-MB-468 and MV4-11 cells. Compound 31 also displayed reasonable kinome selectivity against a panel of 606 wild-type kinases at 1 μM. Moreover, compound 31 exhibited suitable preclinical pharmacokinetic parameters and a promising pharmacodynamic profile. Further, compound 31 showed good antitumor efficacy in MDA-MB-468 xenograft model with no obvious toxicity. Overall, compound 31 was identified as a potential Mps1 inhibitor for cancer therapy and deserve further research.

Metabolomics-based discovery of XHP as a CYP3A4 inhibitor against pancreatic cancer

Background: Xihuang Wan (XHW), a purgative and detoxifying agent, is commonly utilized in modern medicine as a treatment and adjuvant therapy for various malignancies, including breast cancer, liver cancer, and lung cancer. A clinical study demonstrated the potential usefulness of the combination of XHW and gemcitabine as a therapy for pancreatic cancer (PC), indicating that XHW’s broad-spectrum antitumor herbal combination could be beneficial in the treatment of PC. However, the precise therapeutic efficacy of XHW in treating pancreatic cancer remains uncertain.

Aim: This study assessed the biological activity of XHW by optimizing the therapeutic concentration of XHW (Xihuang pills, XHP). We performed cell culture and developed an animal test model to determine whether XHP can inhibit pancreatic cancer (PC). We also applied the well-known widely targeted metabolomics analysis and conducted specific experiments to assess the feasibility of our method in PC therapy.

Materials and Methods: We used UPLC/Q-TOF-MS to test XHP values to set up therapeutic concentrations for the in vivo test model. SW1990 pancreatic cancer cells were cultured to check the effect the anti-cancer effects of XHP by general in vitro cell analyses including CCK-8, Hoechst 33258, and flow cytometry. To develop the animal model, a solid tumor was subcutaneously formed on a mouse model of PC and assessed by immunohistochemistry and TUNEL apoptosis assay. We also applied the widely targeted metabolomics method following Western blot and RT-PCR to evaluate multiple metabolites to check the therapeutic effect of XHP in our cancer test model.

Results: Quantified analysis from UPLC/Q-TOF-MS showed the presence of the following components of XHP: 11-carbonyl-β-acetyl-boswellic acid (AKBA), 11-carbonyl-β-boswellic acid (KBA), 4-methylene-2,8,8-trimethyl-2-vinyl-bicyclo [5.2.0]nonane, and (1S-endo)-2-methyl-3-methylene-2-(4-methyl-3-3-pentenyl)-bicyclo [2.2.1heptane]. The results of the cell culture experiments demonstrated that XHP suppressed the growth of SW1990 PC cells by enhancing apoptosis. The results of the animal model tests also indicated the suppression effect of XHP on tumor growth. Furthermore, the result of the widely targeted metabolomics analysis showed that the steroid hormone biosynthesis metabolic pathway was a critical factor in the anti-PC effect of XHP in the animal model. Moreover, Western blot and RT-PCR analyses revealed XHP downregulated CYP3A4 expression as an applicable targeted therapeutic approach.

Conclusion: The results of this study demonstrated the potential of XHP in therapeutic applications in PC. Moreover, the widely targeted metabolomics method revealed CYP3A4 is a potential therapeutic target of XHP in PC control. These findings provide a high level of confidence that XHP significantly acts as a CYP3A4 inhibitor in anti-cancer therapeutic applications.

Screening of potential hub genes and key pathways associated with breast cancer by bioinformatics tools

Breast cancer (BC) remains the leading cause of cancer-related death in women worldwide. The development of new targeted therapies that may improve patient survival remains an area of growing interest. This study aimed to identify new biomarkers involved in BC progression that could be used as potential targeted therapies. DEGs were selected from three gene expression profiles, GSE55715, GSE124646, and GSE87049, using the GEO2R tool and Venn diagram software. Gene Ontology and KEGG pathways were then performed using DAVID software. Next, the PPI network was constructed using STRING and visualized using Cytoscape software, and hub genes were extracted using the cytoHubba plug-in. Survival analysis was performed using the Kaplan-Meier Plotter, while the expression of hub genes in BC was verified using the GEPIA2 tool. Finally, transcription the factors of hub genes were determined using the NetworkAnalyst database, and the TIMER tool was employed to explore the infiltration levels of tumor immune cells with related genes. A total of 146 DEGs were identified in the three datasets, including 60 upregulated genes that were enriched in the cell cycle, and 86 downregulated genes that were mainly enriched in the TNF signaling pathway and pathways in cancer. Ten genes were identified: BUB1, CDK1, HMMR, MAD2L1, CEP55, AURKA, CCNB2, TPX2, MELK, and KIF20A. The overexpression of hub genes, except CDK1, was associated with poor survival in BC and was regulated by several transcription factors involved in DNA binding activity and transcription regulation. The infiltration levels of immune cells were positively correlated with hub genes, particularly macrophages and CD4+ T cells. This study identified new reliable molecular biomarkers that can serve as potential therapeutic targets for BC treatment.

Identification of risk for ovarian disease enhanced by BPB or BPAF exposure

The ban on bisphenol A (BPA) has led to a rapid increase in the use of BPA analogs, and they are increasingly being detected in the natural environment and biological organisms. Studies have pointed out that BPA analogs can lead to adverse health outcomes. However, their interference with ovarian tissue has not been fully elucidated. In this study, seven- to eight-week-old CD-1 mice were exposed to corn oil containing 300 μg/kg/day bisphenol B (BPB) or bisphenol AF (BPAF) through oral gavage, and ovarian tissues were collected at 14 and 28 days of exposure. Ovarian toxicity was evaluated by the ovarian index, ovarian area, and follicle number. mRNA-seq was used to identify differentially expressed genes (DEGs) and infer the association of DEGs with ovarian diseases. BPB or BPAF exposure induced morphological changes in ovarian tissue in CD-1 mice. In addition, Gene Ontology (GO) analysis revealed disturbances in biological processes (BP) associated with steroid biosynthetic process (GO:0006694) and cellular calcium ion homeostasis (GO:0006874). Subsequently, regulatory networks of BPA analogs (BPB or BPAF)-DEGs-ovarian diseases were constructed. Importantly, the expression levels of DEGs and transcription factors (TFs) associated with ovarian disease were altered. BPB or BPAF exposure causes damage to ovarian morphology through the synergistic effects of multiple biological processes and may be associated with altered mRNA expression profiles as a risk factor for ovarian diseases.

Artificial intelligence applications in pediatric oncology diagnosis

Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.

Clinical value and potential mechanisms of BUB1B up-regulation in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) has insidious onset, late clinical diagnosis and high recurrence rate, which leads to poor quality of patient life. Therefore, it is necessary to further explore the pathogenesis and therapy targets of NPC. BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) was found to be up-regulated in a variety of cancers, but only two previous study showed that BUB1B was overexpressed in NPC and the sample size was small. The clinical role of BUB1B expression and its underlying mechanism in NPC require more in-depth research. Immunohistochemical samples and public RNA-seq data indicated that BUB1B protein and mRNA expression levels were up-regulated in NPC, and summary receiver operating characteristic curve indicated that BUB1B expression level had a strong ability to distinguish NPC tissues from non-NPC tissues. Gene ontology and Kyoto Encyclopedia of genes and genomes were performed and revealed that BUB1B and its related genes were mainly involved in cell cycle and DNA replication. Protein- Protein Interaction were built to interpret the BUB1B molecular mechanism. Histone deacetylase 2 (HDAC2) could be the upstream regulation factor of BUB1B, which was verified by Chromatin Immunoprecipitation Sequencing samples. In summary, BUB1B was highly expressed in NPC, and HDAC2 may affect cell cycle by regulating BUB1B to promote cancer progression.

Identification of an Individualized Prognostic Biomarker for Serous Ovarian Cancer: A Qualitative Model

Serous ovarian cancer is the most common type of ovarian epithelial cancer and usually has a poor prognosis. The objective of this study was to construct an individualized prognostic model for predicting overall survival in serous ovarian cancer. Based on the relative expression orderings (Ea > Eb/Ea ≤ Eb) of gene pairs closely associated with serous ovarian prognosis, we tried constructing a potential individualized qualitative biomarker by the greedy algorithm and evaluated the performance in independent validation datasets. We constructed a prognostic biomarker consisting of 20 gene pairs (SOV-P20). The overall survival between high- and low-risk groups stratified by SOV-P20 was statistically significantly different in the training and independent validation datasets from other platforms (p < 0.05, Wilcoxon test). The average area under the curve (AUC) values of the training and three validation datasets were 0.756, 0.590, 0.630, and 0.680, respectively. The distribution of most immune cells between high- and low-risk groups was quite different (p < 0.001, Wilcoxon test). The low-risk patients tended to show significantly better tumor response to chemotherapy than the high-risk patients (p < 0.05, Fisher’s exact test). SOV-P20 achieved the highest mean index of concordance (C-index) (0.624) compared with the other seven existing prognostic signatures (ranging from 0.511 to 0.619). SOV-P20 is a promising prognostic biomarker for serous ovarian cancer, which will be applicable for clinical predictive risk assessment.

Comprehensive bioinformatics analysis confirms RBMS3 as the central candidate biological target for ovarian cancer

Ovarian cancer (OC) is one of the most lethal malignancies in the female reproductive system. To find genes related to cancer progression targeting specific biological factors for targeted therapy, bioinformatics technology has been widely used. To screen the prognostic gene markers of OC by bioinformatics and explore their potential molecular biological mechanisms. Two data sets related to OC, GSE54388, and GSE119056, were rooted in the open comprehensive gene expression database (GEO). To correct the background of the data, standardize and screen differentially expressed genes (DEGs) using the R software limma package. The selected DEGs were enriched by Gene Ontology (GO) and through DAVID online database. Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis and protein-protein interaction network (PPI-network) map were constructed by STRING online database and Cytoscape software. Combined with the TCGA database, univariate and multivariate COX regression were used to screen prognostic genes. QRT-PCR was used to verify DEGs in clinical tissue samples. Eventually, the function of RBMS3 on the viability, migration, invasion, and apoptosis of OC cells was tested through functional experiments in vitro. 352 common DEGs were screened from GSE54388 and GSE119056 data sets. Survival analysis showed that MEIS2, TSTA3, CNTN1, RBMS3, and TRA2A were considered to be connected with the prognosis of OC. We discover that the expression level of RBMS3 was positively connected with the overall survival (OS) rate of sufferers with OC. The level of RBMS3 in OC tissues was markedly lower than that in neighboring structures and the outcomes of the GEPIA database were consistent with those of the qRT-PCR experiment. Through gene transfection technology it was found that overexpression of RBMS3 in OC cells substantially suppressed the vitality, migration, and invasion of OC cells and raised the rates of apoptosis in the OC cells. In this experiment, we distinguish 5 genes that may participate in the prognosis of OC and showed the key genes and pathways related to OC. It is speculated that RBMS3, a tumor suppressor gene, can be applied as a potential biological marker for the treatment of OC, gene expression summary, and prognosis.

Intimate intertwining of the pathogenesis of hypoxia and systemic sclerosis: A transcriptome integration analysis

OBJECTIVES

Systemic sclerosis (SSc) is an autoimmune disease caused by various pathogenic factors, including hypoxia. Hypoxia stimulates the production of the extracellular matrix to promote fibrosis. However, the integrated function and the underlying mechanism of hypoxia in SSc are unclear.

METHODS

In the present study, we used Agilent SurePrint G3 Human Gene Expression v3 for the transcriptional sequencing of fibroblasts with and without hypoxia to detect differentially expressed genes (DEGs) in hypoxia. We analyzed the results with the transcriptome data of SSc lesions (GSE95065) to select the co-DEGs. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on the basis of the co-DEGs using the R package ClusterProfiler, which showed that hypoxia and cross talk of hypoxia with other pathogenic factors are involved in the pathogenesis of SSc. Furthermore, we constructed a protein-protein interaction (PPI) network of co-DEGs and screened two significant functional expression modules.

RESULTS

We identified nine hub genes (ALDH1A1, EGF, NOX4, LYN, DNTT, PTGS2, TKT, ACAA2, and ALDH3A1). These genes affect the pentose phosphate pathway, oxidative stress, and lipolysis.

CONCLUSION

Our study provides insights into the mechanisms underlying the effects of hypoxia on SSc pathogenesis, which will help to better understand SSc pathogenesis and develop new therapeutic strategies for SSc.

Bioinformatic identification of differentially expressed genes associated with hepatocellular carcinoma prognosis

Hepatocellular carcinoma (HCC) is still a significant global health problem. The development of bioinformatics may provide the opportunities to identify novel therapeutic targets. This study bioinformatically identified the differentially expressed genes (DEGs) in HCC and associated them with HCC prognosis using data from published databases. The DEGs downloaded from the Gene Expression Omnibus (GEO) website were visualized using the Venn diagram software, and then subjected to the GO and KEGG analyses, while the protein-protein interaction network was analyzed using Cytoscape software with the Search Tool for the search tool for the retrieval of interacting genes and the molecular complex detection plug-in. Kaplan-Meier curves and the log rank test were used to associate the core PPI network genes with the prognosis. There were 57 upregulated and 143 downregulated genes in HCC samples. The GO and pathway analyses revealed that these DEGs are involved in the biological processes (BPs), molecular functions (MFs), and cell components (CCs). The PPI network covered 50 upregulated and 108 downregulated genes, and the core modules of this PPI network contained 34 upregulated genes. A total of 28 of these upregulated genes were associated with a poor HCC prognosis, 27 of which were highly expressed in HCC tissues. This study identified 28 DEGs to be associated with a poor HCC prognosis. Future studies will investigate their possible applications as prognostic biomarkers and potential therapeutic targets for HCC.

Chrysin-Induced G Protein-Coupled Estrogen Receptor Activation Suppresses Pancreatic Cancer

Pancreatic cancer (PC) has a high mortality rate due to its poor prognosis and the possibility of surgical resection in patients with the disease. Importantly, adjuvant chemotherapy is necessary to improve PC prognosis. Chrysin, a natural product with anti-inflammatory, antioxidant, and anticancer properties, has been studied for several years. Our previous study demonstrated that chrysin induced G protein-coupled estrogen receptor (GPER) expression and regulated its activity in breast cancer. Herein, we investigated whether chrysin-induced GPER activation suppresses PC progression in MIA PaCa-2 cells and a xenograft model. To determine its mechanism of action, cytotoxicity and clonogenic assays, a FACS analysis, and Western blotting were performed. Furthermore, the delay in tumor growth was evaluated in the MIA PaCa-2-derived xenograft model. Tumor tissues were investigated by Western blotting, immunohistochemistry, and a proteomic analysis. Chrysin caused cell cycle arrest and significantly decreased cell viability. Following co-treatment with chrysin and 17β-estradiol, the inhibitory effect of chrysin on cell proliferation was enhanced. In the xenograft model, chrysin and G1 (a GPER agonist) significantly delayed tumor growth and reduced both Ki-67 (a proliferation marker) and c-Myc expressions in tumor tissues. The proteomic analysis of tumor tissues identified that rho-associated coiled-coil containing protein kinase 1 (ROCK1), transgelin 2 (TAGLN2), and FCH and Mu domain containing endocytic adaptor 2 (FCHO2) levels were significantly reduced in chrysin-treated tumor tissues. High ROCK1, TAGLN2, and FCHO2 expressions were indicative of low overall PC survival as found using the Kaplan–Meier plotter. In conclusion, our results suggest that chrysin suppresses PC progression through the activation of GPER and reductions in ROCK1, TAGLN2, and FCHO2 expressions.

Identification and Validation of a Potential Stemness-Associated Biomarker in Hepatocellular Carcinoma

Background

Cancer stem cells (CSCs) are typically related to metastasis, recurrence, and drug resistance in malignant tumors. However, the biomarker and mechanism of CSCs need further exploration. This study is aimed at comprehensively depicting the stemness characteristics and identify a potential stemness-associated biomarker in hepatocellular carcinoma (HCC).

Methods

The data of HCC patients from The Cancer Genome Atlas (TCGA) were collected and divided based on the mRNA expression-based stemness index (mRNAsi) in this study. Weighted gene coexpression network analysis (WGCNA) and the protein-protein interaction (PPI) network were performed, and the genes were screened through the Cytoscape software. Then, we constructed a prognostic expression signature using the multivariable Cox analysis and verified using the GEO and ICGC databases. Even more importantly, we used the three-dimensional (3D) fibrin gel to enrich the tumor-repopulating cells (TRCs) to validate the expression of the signature in CSCs by quantitative RT-PCR.

Results

mRNAsi was significantly elevated in tumor and high-mRNAsi score was associated with poor overall survival in HCC. The positive stemness-associated (blue) module with 737 genes were screened based on WGCNA, and Budding uninhibited by benzimidazoles 1 (BUB1) was identified as the hub gene highly related to stemness in HCC. Then, the prognostic value and stemness characteristics were well validated in the ICGC and GSE14520 cohorts. Further analysis showed the expression of BUB1 was elevated in TRCs.

Conclusion

BUB1, as a potential stemness-associated biomarker, could serve as a therapeutic CSCs-target and predicted the clinical outcomes of patients with HCC.

Reduction of LPAR1 Expression in Neuroblastoma Promotes Tumor Cell Migration

Neuroblastoma is the most common extracranial solid tumor in children. Tumor metastasis in high-risk NB patients is an essential problem that impairs the survival of patients. In this study, we aimed to use a comprehensive bioinformatics analysis to identify differentially expressed genes between NB and control cells, and to explore novel prognostic markers or treatment targets in tumors. In this way, FN1, PIK3R5, LPAR6 and LPAR1 were screened out via KEGG, GO and PPI network analysis, and we verified the expression and function of LPAR1 experimentally. Our research verified the decreased expression of LPAR1 in NB cells, and the tumor migration inhibitory effects of LPA on NB cells via LPAR1. Moreover, knockdown of LPAR1 promoted NB cell migration and abolished the migration-inhibitory effects mediated by LPA-LPAR1. The tumor-suppressing effects of the LPA-LPAR1 axis suggest that LPAR1 might be a potential target for future treatment of NB.

Integrated Bioinformatics Analysis for Identifying the Significant Genes as Poor Prognostic Markers in Gastric Adenocarcinoma

Gastric adenocarcinoma (GAC) is the most common histological type of gastric cancer and imposes a considerable health burden globally. The purpose of this study was to identify significant genes and key pathways participated in the initiation and progression of GAC. Four datasets (GSE13911, GSE19826, GSE54129, and GSE79973) including 171 GAC and 77 normal tissues from Gene Expression Omnibus (GEO) database were collected and analyzed. Through integrated bioinformatics analysis, we obtained 69 commonly differentially expressed genes (DEGs) among the four datasets, including 20 upregulated and 49 downregulated genes. The prime module in protein-protein interaction network of DEGs, including ADAMTS2, COL10A1, COL1A1, COL1A2, COL8A1, BGN, and SPP1, was enriched in protein digestion and absorption, ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and amoebiasis. Furthermore, expression and survival analysis found that all seven hub genes were highly expressed in GAC tissues and 6 of them (except for SPP1) were able to predict poor prognosis of GAC. Finally, we verified the 6 high-expressed hub genes in GAC tissues via immunohistochemistry, Western blot, and RNA quantification analysis. Altogether, we identified six significantly upregulated DEGs as poor prognostic markers in GAC based on integrated bioinformatical methods, which could be potential molecular markers and therapeutic targets for GAC patients.

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning.

Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level.

Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis.

Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.

Construction of lncRNA-miRNA-mRNA network based on ceRNA mechanism reveals the function of lncRNA in the pathogenesis of gout

Objective

To identify differentially expressed lncRNA, miRNA, and mRNA during the pathogenesis of gout, explore the ceRNA network regulatory mechanism of gout, and seek potential therapeutic targets.

Method

First, gout‐related chips were retrieved by GEO database. Then, the analysis of differentially expressed lncRNAs and mRNAs was conducted by R language and other software. Besides, miRNA and its regulated mRNA were predicted based on public databases, the intersection of differentially expressed mRNA and predicated mRNA was taken, and the lncRNA‐miRNA‐mRNA regulatory relationships were obtained to construct the ceRNA regulatory network. Subsequently, hub genes were screened by the STRING database and Cytoscape software. Then the DAVID database was used to illustrate the gene functions and related pathways of hub genes and to mine key ceRNA networks.

Results

Three hundred and eighty‐eight lncRNAs and 758 mRNAs were identified with significant differential expression in gout patient, which regulates hub genes in the ceRNA network, such as JUN, FOS, PTGS2, NR4A2, and TNFAIP3. In the ceRNA network, lncRNA competes with mRNA for miRNA, thus affecting the IL‐17 signaling pathway, TNF signaling pathway, Oxytocin signaling pathway, and NF‐κB signaling pathway through regulating the cell's response to chemical stress. The research indicates that five miRNAs (miR‐429, miR‐137, miR‐139‐5p, miR‐217, miR‐23b‐3p) and five lncRNAs (SNHG1, FAM182A, SPAG5‐AS1, HNF1A‐AS1, UCA1) play an important role in the formation and development of gout.

Conclusion

The interaction in the ceRNA network can affect the formation and development of gout by regulating the body's inflammatory response as well as proliferation, differentiation, and apoptosis of chondrocytes and osteoclasts. The identification of potential therapeutic targets and signaling pathways through ceRNA network can provide a reference for further research on the pathogenesis of gout.

Network Pharmacology and Molecular Docking to Elucidate the Potential Mechanism of Ligusticum Chuanxiong Against Osteoarthritis

Background: Osteoarthritis (OA) is a degenerative disease which serious affects patients. Ligusticum chuanxiong (CX) has been shown to have a certain curative effect on osteoarthritis in traditional Chinese medicine therapy. This study is based on network pharmacology and molecular docking technology to explore the potential mechanism of CX.

Methods: Components of CX to treat osteoarthritis were screened in the TCMSP database and targets were predicted by the PharmMapper database, the osteoarthritis targets were collected from the GeneCards database, and intersection genes were found to be the possible targets of CX anti-OA. The STRING database and Cytoscape software were utilized for protein-protein interaction analysis and further screening of core targets. The Metascape database was used for KEGG and GO enrichment analyses. Then, the top 10 pathways were selected to construct “drug-compound-target-pathway-disease” network analysis. Finally, molecular docking was used to analyze the binding affinity of seven compounds with core targets and TNF-α.

Results: Seven compounds with 253 non-repetitive targets of CX were screened from the TCMSP database and 60 potential intersection targets of CX anti-OA were found. PPI network analysis showed that the core targets were ALB, AKT1, IGF1, CASP3, MAPK1, ANXA5, and MAPK14, while GO and KEGG pathway enrichment analyses showed that the relevant biological processes involved in the treatment of osteoarthritis by CX might include the MAPK cascade and reactive oxygen species metabolic process. The KEGG pathway analysis result was mainly associated with the MAPK signaling pathway and PI3K-AKT signaling pathway. We further docked seven ingredients with MAPK1 and MAPK14 enriched in the MAPK pathway, and TNF-α as the typical inflammatory cytokine. The results also showed good binding affinity, especially FA, which may be the most important component of CX anti-OA.

Conclusion: Our research revealed the potential mechanism of CX in the treatment of OA, and our findings can also pave the way for subsequent basic experimental verification and a new research direction.

Bioinformatics Characterization of Candidate Genes Associated with Gene Network and miRNA Regulation in Esophageal Squamous Cell Carcinoma Patients

The present study aimed to identify potential therapeutic targets for esophageal squamous cell carcinoma (ESCC). The gene expression profile GSE161533 contained 84 samples, in that 28 tumor tissues and 28 normal tissues encoded as ESCC patients were retrieved from the Gene Expression Omnibus database. The obtained data were validated and screened for differentially expressed genes (DEGs) between normal and tumor tissues with the GEO2R tool. Next, the protein–protein network (PPI) was constructed using the (STRING 2.0) and reconstructed with Cytoscape 3.8.2, and the top ten hub genes (HGsT10) were predicted using the Maximal Clique Centrality (MCC) algorithm of the CytoHubba plugin. The identified hub genes were mapped in GSE161533, and their expression was determined and compared with The Cancer Genome Atlas (TCGA.) ESCC patient’s samples. The overall survival rate for HGsT10 wild and mutated types was analyzed with the Gene Expression Profiling Interactive Analysis2 (GEPIA2) server and UCSC Xena database. The functional and pathway enrichment analysis was performed using the WebGestalt database with the reference gene from lumina human ref 8.v3.0 version. The promoter methylation for the HGsT10 was identified using the UALCAN server. Additionally, the miRNA-HGsT10 regulatory network was constructed to identify the top ten hub miRNAs (miRT10). Finally, we identified the top ten novel driving genes from the DEGs of GSE161533 ESCC patient’s sample using a multi-omics approach. It may provide new insights into the diagnosis and treatment for the ESCC affected patients early in the future.

Integrated Bioinformatics Analysis for Identification of the Hub Genes Linked with Prognosis of Ovarian Cancer Patients

BACKGROUND

One of the most usual gynecological state of tumor is ovarian cancer and is a major reason of gynecological tumor-related global mortality rate. There have been multiple risk elements related to ovarian cancer like the background of past cases associated with breast cancer or ovarian cancer, or excessive body weight issues, case history of smoking, and untimely menstruation or menopause. Because of unclear expressions, more than 70% of the ovarian cancer patient cases are determined during the early stage. Material and Methods. GSE38666, GSE40595, and GSE66957 were the three microarray datasets which were analyzed using GEO2R for screening the differentially expressed genes. GO, Kyoto Encyclopedia of Genes, and protein expression studies were performed for analysis of hub genes. Then, survival analysis was performed for all the hub genes.

RESULTS

From the dataset, a total of 199 differentially expressed genes (DEGs) were identified. Through the KEGG pathway study, it was noted that the DEGs are mainly linked with the AGE-RAGE signaling pathway, central carbon metabolism, and human papillomavirus infection. The survival analysis showed 4 highly expressed hub genes COL4A1, SDC1, CDKN2A, and TOP2A which correlated with overall survival in ovarian cancer patients. Moreover, the expression of the 4 hub genes was validated by the GEPIA database and the Human Protein Atlas.

CONCLUSION

The results have shown that all 4 hub genes were found to be upregulated in ovarian cancer tissues which predict poor prognosis in patients with ovarian cancer.

A Novel Glycosyltransferase-Related Gene Signature for Overall Survival Prediction in Patients with Ovarian Cancer

Background

Ovarian cancer is a highly malignant epithelial tumor. Recently, it has been reported the role of glycosyltransferases (GTs) in various cancers. However, the prognostic value of GTs-related genes in ovarian cancer remained largely unknown.

Methods

RNA-sequencing (RNA-seq) data and corresponding clinical characteristics of patients with ovarian cancer were extracted from the public database of the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). We constructed the least absolute shrinkage and selection operator (LASSO) Cox regression model to explore a multigene signature comprising GTs-related genes in the TCGA and GTEx cohort. Patients with ovarian cancer in International Cancer Genome Consortium (ICGC) database were applied for further validation. We also performed functional analysis on the differentially expressed genes (DEGs) of high-risk and low-risk groups in the TCGA cohort. Additionally, the immune status between the two risk groups was assessed, respectively.

Results

Our results showed that 64 GTs-related genes were differentially expressed between tumor tissues and normal tissues in the TCGA and GTEx cohort. A prognostic model of 15 candidate genes was constructed, which classified patients into high- and low-risk groups. Compared with low-risk patients, high-risk patients had an obvious worse overall survival (OS) (P < 0.0001 in the TCGA and GTEx cohort and P = 0.042 in the ICGC cohort). Multivariate Cox regression analysis revealed that the risk score was an independent factor for OS of ovarian cancer. Functional analysis indicated that these DEGs were also enriched in immune-related pathways, and the immune status was significantly different between the two risk groups in TCGA cohort.

Conclusion

In conclusion, a novel GTs-related gene signature may be used for the prognosis of ovarian cancer. Targeting GTs-related gene can act as a therapeutic alternative for ovarian cancer.

Identification of mitochondria-related hub genes in sarcopenia and functional regulation of MFG-E8 on ROS-mediated mitochondrial dysfunction and cell cycle arrest

Sarcopenia has high prevalence in the elderly population, but the genes and pathways related to aging in elderly patients with sarcopenia are poorly understood. Milk fat globule epidermal growth factor 8 (MFG-E8) is a peripheral membrane glycoprotein isolated from the milk fat globule membrane (MFGM). It has been found to exhibit various nutritional effects, including antibacterial, anti-cancer, anti-oxidant, anti-sarcopenia, and improving brain development and cognitive effects. This study aimed to investigate key differentially expressed genes (DEGs) and pathways associated with the progression of sarcopenia using bioinformatics analysis and in vitro myoblast experiment. The gene expression profiles of GSE8479 and GSE9676, which includes 40 young normal samples and 55 elderly samples, were downloaded from the Gene Expression Omnibus Database (GEO). Over 3253 DEGs were identified in the young and elderly samples (adjusted p value <0.05). A total of 213 co-expressed significantly DEGs were identified with Venn diagrams, including 82 up-regulated DEGs and 131 down-regulated DEGs. Based on the analysis of Gene Ontology (GO), protein-protein interaction (PPI) networks and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, 10 hub genes screened by our study have been proved to play a role in regulating the occurrence and development of aging-related sarcopenia mainly via metabolic pathways, Huntington's disease, Parkinson's disease, oxidative phosphorylation and non-alcoholic fatty liver disease pathways. To further verify the protective effect of MFG-E8 on oxidative stress injured myoblasts, the cell cycle distribution, cell viability and reactive oxygen species (ROS) production were measured. The protein and mRNA levels of Akt, extracellular regulated protein kinases (ERK), p21Cip1, p27Kip1, cyclin D1, cyclin E1, cyclin-dependent kinase (CDK) 2 and 4 were quantified using qRT-PCR and western blot analysis. The results indicated that MFG-E8 has potential anti-sarcopenia effects by promoting ERK and Akt activation-mediated cell proliferation and cell cycle progression in myoblasts.

MicroRNA miR-23b-3p promotes osteosarcoma by targeting ventricular zone expressed PH domain-containing 1 (VEPH1)/phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) pathway

Increasing evidence suggests that dysregulated miRNA expression can lead to the tumorigenesis of osteosarcoma (OS). Nevertheless, the potential role of miR-23b-3p in OS is unclear and remains to be explored. Microarray analysis was performed to identify key genes involved in OS. Reverse transcription quantitative polymerase chain reaction and Western blotting were used to examine miR-23b-3p expression, ventricular zone expressed PH domain-containing 1 (VEPH1) transcript (as well as other transcripts as indicated), and phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) signaling pathway-related protein expression. A luciferase reporter gene assay was performed to confirm the regulatory relationship between VEPH1 mRNA and miR-23b-3p. Cell viability was evaluated using the Cell Counting Kit-8 assay, cell growth was assessed using the bromodeoxyuridine enzyme-linked immunosorbent assay, and cell migration was tested using a wound healing assay. We found significant upregulation of miR-23b-3p in OS, which prominently promoted the viability, proliferation, and migration of OS cells. Additionally, VEPH1 was found to be a target of miR-23b-3p and its expression was decreased in OS. Lastly, VEPH1 alleviated the promotion effect of miR-23b-3p on the malignancy phenotypes of OS cells via the PI3K/AKT signaling pathway. Thus, miR-23b-3p augmented the viability, proliferation, and migration of OS cells by directly targeting and downregulating VEPH1, which inhibited the activation of the PI3K/AKT signaling pathway.

Human iPSC-derived fallopian tube organoids with BRCA1 mutation recapitulate early-stage carcinogenesis

Germline pathogenic mutations in BReast CAncer (BRCA1) genes are thought to drive normal fallopian tube epithelial (FTE) cell transformation to high-grade serous ovarian cancer. No human models capture the sequence of events for disease initiation and progression. Here, we generate induced pluripotent stem cells (iPSCs) from healthy individuals and young ovarian cancer patients with germline pathogenic BRCA1 mutations (BRCA1^mut). Following differentiation into FTE organoids, BRCA1^mut lines exhibit cellular abnormalities consistent with neoplastic transformation compared to controls. BRCA1^mut organoids show an increased production of cancer-specific proteins and survival following transplantation into mice. Organoids from women with the most aggressive ovarian cancer show the greatest pathology, indicating the potential value to predict clinical severity prior to disease onset. These human FTE organoids from BRCA1^mut carriers provide a faithful physiological in vitro model of FTE lesion generation and early carcinogenesis. This platform can be used for personalized mechanistic and drug screening studies.

Yucer et al. generate a human BRCA1 mutant iPSC-derived fallopian tube organoid model, which recapitulates BRCA1 mutant ovarian carcinogenesis in vitro and shows tumors in vivo. This model provides a biologically relevant platform to validate drugs and a basis for personalized early detection and preventative strategies for women carrying BRCA1 mutations.

Identification of keygenes, miRNAs and miRNA-mRNA regulatory pathways for chemotherapy resistance in ovarian cancer

Background

Chemotherapy resistance, especially platinum resistance, is the main cause of poor prognosis of ovarian cancer. It is of great urgency to find molecular markers and mechanism related to platinum resistance in ovarian cancer.

Methods

One mRNA dataset (GSE28739) and one miRNA dataset (GSE25202) were acquired from Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) between platinum-resistant and platinum-sensitive ovarian cancer patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs were performed using the DAVID to present the most visibly enriched pathways. Protein–protein interaction (PPI) of these DEGs was constructed based on the information of the STRING database. Hub genes related to platinum resistance were visualized by Cytoscape software. Then, we chose seven interested hub genes to further validate using qRT-PCR in A2780 ovarian cancer cell lines. And, at last, the TF-miRNA-target genes regulatory network was predicted and constructed using miRNet software.

Results

A total of 63 upregulated DEGs, 124 downregulated DEGs, four upregulated miRNAs and six downregulated miRNAs were identified. From the PPI network, the top 10 hub genes were identified, which were associated with platinum resistance. Our further qRT-PCR showed that seven hub genes (BUB1, KIF2C, NUP43, NDC80, NUF2, CCNB2 and CENPN) were differentially expressed in platinum-resistant ovarian cancer cells. Furthermore, the upstream transcription factors (TF) for upregulated DE-miRNAs were SMAD4, NFKB1, SMAD3, TP53 and HNF4A. Three overlapping downstream target genes (KIF2C, STAT3 and BUB1) were identified by miRNet, which was regulated by hsa-miR-494.

Conclusions

The TF-miRNA–mRNA regulatory pairs, that is TF (SMAD4, NFKB1 and SMAD3)-miR-494-target genes (KIF2C, STAT3 and BUB1), were established. In conclusion, the present study is of great significance to find the key genes of platinum resistance in ovarian cancer. Further study is needed to identify the mechanism of these genes in ovarian cancer.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Application of Bioinformatics Analysis to Identify Important Pathways and Hub Genes in Ovarian Cancer Affected by WT1

Wilms tumor gene (WT1) is used as a marker for the diagnosis and prognosis of ovarian cancer. However, the molecular mechanisms involving WT1 in ovarian cancer require further study. Herein, we used bioinformatics and other methods to identify important pathways and hub genes in ovarian cancer affected by WT1. The results showed that WT1 is highly expressed in ovarian cancer and is closely related to the overall survival and progression-free survival (PFS) of ovarian cancer. In ovarian cancer cell line SKOV3, WT1 downregulation increased the mRNA expression of 638 genes and decreased the mRNA expression of 512 genes, which were enriched in the FoxO, AMPK, and the Hippo signaling pathways. The STRING online tool and Cytoscape software were used to construct a Protein-protein interaction (PPI) network and for Module analysis, and 18 differentially expressed genes (DEGs) were selected. Kaplan-Meier plotter analysis revealed that 16 of 18 genes were related to prognosis. Analysis of GEPIA datasets indicated that 7 of 16 genes were differentially expressed in ovarian cancer tissues and in normal tissues. The expression of IGFBP1 and FBN1 genes increased significantly after WT1 interference, while the expression of the SERPINA1 gene decreased significantly. The correlation between WT1 expression and that of these three genes was consistent with that of ovarian cancer tissues and normal tissues. According to the GeneMANIA online website analysis, there were complex interactions between WT1, IGFBP1, FBN1, SERPINA1, and 20 other genes. In conclusion, we have identified important signaling pathways involving WT1 that affect ovarian cancer, and distinguished three differentially expressed genes regulated by WT1 associated with the prognosis of ovarian cancer. Our findings provide evidence outlining mechanisms involving WT1 gene expression in ovarian cancer and provides a rational for novel treatment of ovarian cancer.

Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis

Ovarian cancer is one of the leading causes of gynecological malignancy-related deaths. The underlying molecular development mechanism has however not been elucidated. In this study, we used bioinformatics to reveal critical molecular and biological processes associated with ovarian cancer. The microarray datasets of miRNA and mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Besides, we performed target prediction of the identified differentially expressed miRNAs. The overlapped differentially expressed genes (DEGs) were obtained combined with miRNA targets predicted and the DEGs identified from the mRNA dataset. The Cytoscape software was used to design a regulatory network of miRNA-gene. Moreover, the overlapped DEGs in the network were subjected to enrichment analysis to explore the associated biological processes. The molecular protein-protein interaction (PPI) network was used to identify the key genes among the DEGs of prognostic value for ovarian cancer, and the genes were evaluated via Kaplan-Meier curve analysis. A total of 186 overlapped DEGs were identified. Through miRNA-gene network analysis, we found that miR-195-5p, miR-424-5p, and miR-497-5p highly exhibited targeted association with overlapped DEGs. The three miRNAs are critical in the regulatory network and act as tumor suppressors. The overlapped DEGs were mainly associated with protein metabolism, histogenesis, and development of the reproductive system and ocular tissues. The PPI network identified 10 vital genes that promote tumor progression. Survival analysis found that CEP55 and CCNE1 may be associated with the prognosis of ovarian cancer. These findings provide insights to understand the pathogenesis of ovarian cancer and suggest new candidate biomarkers for early screening of ovarian cancer.

Identification of Hub mRNAs and lncRNAs in Atrial Fibrillation Using Weighted Co-expression Network Analysis With RNA-Seq Data

Atrial fibrillation (AF)/paroxysmal AF (PAF) is the main cause of cardiogenic embolism. In recent years, the progression from paroxysmal AF to persistent AF has attracted more and more attention. However, the molecular mechanism of the progression of AF is unclear. In this study, we performed RNA sequencing for normal samples, paroxysmal AF and persistent AF samples to identify differentially expressed gene (DEG) and explore the roles of these DEGs in AF. Totally, 272 differently expressed mRNAs (DEmRNAs) and 286 differentially expressed lncRNAs (DElncRNAs) were identified in paroxysmal AF compared to normal samples; 324 DEmRNAs and 258 DElncRNAs were found in persistent atrial fibrillation compared with normal samples; and 520 DEmRNAs and 414 DElncRNAs were identified in persistent AF compared to paroxysmal AF samples. Interestingly, among the DEGs, approximately 50% were coding genes and around 50% were non-coding RNAs, suggesting that lncRNAs may also have a crucial role in the progression of AF. Bioinformatics analysis demonstrated that these DEGs were significantly related to regulating multiple AF associated pathways, such as the regulation of vascular endothelial growth factor production and binding to the CXCR chemokine receptor. Furthermore, weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and hub RNAs and lncRNAs to determine their potential associations with AF. Five hub modules were identified in the progression of AF, including blue, brown, gray, turquoise and yellow modules. Interestingly, blue module and turquoise module were significantly negatively and positively correlated to the progression of AF respectively, indicating that they may have a more important role in the AF. Moreover, the hub protein-protein interaction (PPI) networks and lncRNA-mRNA regulatory network were constructed. Bioinformatics analysis on the hub PPI network in turquoise was involved in regulating immune response related signaling, such as leukocyte chemotaxis, macrophage activation, and positive regulation of α-β T cell activation. Our findings could clarify the underlying molecular changes associated fibrillation, and provide a useful resource for identifying AF marker.

MicroRNA-100 inhibits breast cancer cell proliferation, invasion and migration by targeting FOXA1

MicroRNAs (miRNAs/miRs) are highly conserved single-stranded small non-coding RNAs, which are involved in the physiological and pathological processes of breast cancer, and affect the prognosis of patients with breast cancer. The present study used the Gene Expression Omnibus (GEO)2R tool to detect miR-100 expression in breast cancer tissues obtained from GEO breast cancer-related datasets. Bioinformatics analysis revealed that miR-100 expression was downregulated in different stages, grades and lymph node metastasis stages of breast cancer, and patients with high miR-100 expression had a more favorable prognosis. Based on these analyses, Cell Counting Kit-8, wound healing and Transwell assays were performed, and the results demonstrated that overexpression of miR-100 inhibited the proliferation, migration and invasion of breast cancer cells. To verify the tumor-suppressive effect of miR-100 in breast cancer, the LinkedOmics and PITA databases were used to assess the association between miR-100 and forkhead box A1 (FOXA1). The results demonstrated that miR-100 had binding sites within the FOXA1 gene, and FOXA1 expression was negatively associated with miR-100 expression in breast cancer tissues. Similarly, a negative association was observed between miR-100 and FOXA1 expression, using the StarBase V3.0 database. The association between miR-100 and FOXA1 was further verified via reverse transcription-quantitative PCR and western blot analyses, and the dual-luciferase reporter assay. The results demonstrated that miR-100 targeted the 3′-untranslated region of FOXA1 in breast cancer cells. Furthermore, rescue experiments were performed to confirm whether miR-100 exerts its antitumor effects by regulating FOXA1. The results demonstrated that overexpression of FOXA1 promoted the proliferation, migration and invasion of breast cancer cells; thus, the antitumor effects of miR-100 in breast cancer were reversed following overexpression of FOXA1. Taken together, the results of the present study suggest that miR-100 inhibits the proliferation, migration and invasion of breast cancer cells by targeting FOXA1 expression. These results may provide a novel insight and an experimental basis for identifying effective therapeutic targets of high specificity for breast cancer.

BUB1B and circBUB1B_544aa aggravate multiple myeloma malignancy through evoking chromosomal instability

Multiple myeloma (MM) is an incurable plasma cell malignancy in the bone marrow characterized by chromosome instability (CIN), which contributes to the acquisition of heterogeneity, along with MM progression, drug resistance, and relapse. In this study, we elucidated that the expression of BUB1B increased strikingly in MM patients and was closely correlated with poor outcomes. Overexpression of BUB1B facilitated cellular proliferation and induced drug resistance in vitro and in vivo, while genetic targeting BUB1B abrogated this effect. Mechanistic studies unveiled that enforced expression of BUB1B evoked CIN resulting in MM poor outcomes mainly through phosphorylating CEP170. Interestingly, we discovered the existence of circBUB1B_544aa containing the kinase catalytic center of BUB1B, which was translated by a circular RNA of BUB1B. The circBUB1B_544aa elevated in MM peripheral blood samples was closely associated with MM poor outcomes and played a synergistic effect with BUB1B on evoking CIN. In addition, MM cells could secrete circBUB1B_544aa and interfere the MM microenvironmental cells in the same manner as BUB1B full-length protein. Intriguingly, BUB1B siRNA, targeting the kinase catalytic center of both BUB1B and circBUB1B_544aa, significantly inhibited MM malignancy in vitro and in vivo. Collectively, BUB1B and circBUB1B_544aa are promising prognostic and therapeutic targets of MM.

Bioinformatic Analysis for Influential Core Gene Identification and Prognostic Significance in Advanced Serous Ovarian Carcinoma

Background and objectives: Ovarian cancer is one of the leading causes of death among women worldwide. Most newly diagnosed ovarian cancer patients are diagnosed in advanced stages of the disease. Despite various treatments, most patients with advanced stage ovarian cancer, including serous ovarian cancer (the most common subtype of ovarian cancer), experience recurrence, which is associated with extremely poor prognoses. In the present study, we aimed to identify core genes involved in ovarian cancer and their associated molecular mechanisms, as well as to investigate related clinicopathological implications in ovarian cancer. Materials and methods: Three gene expression cohorts (GSE14407, GSE36668, and GSE38666) were obtained from the Gene Expression Omnibus databases to explore potential therapeutic biomarkers for ovarian cancer. Nine up-regulated and six down-regulated genes were screened. Three publicly available gene expression datasets (GSE14407, GSE36668, and GSE38666) were analyzed. Results: A total of 14 differently expressed genes (DEGs) were identified, among which nine genes were upregulated (BIRC5, CDCA3, CENPF, KIF4A, NCAPG, RRM2, UBE2C, VEGFA, and NR2F6) and were found to be significantly enriched in cell cycle regulation by gene ontology analysis. Further protein–protein interaction network analysis revealed seven hub genes among these DEGs. Moreover, Kaplan–Meier survival analysis showed that a higher expression of CDCA3 and UBE2C was associated with poor overall patient survival regardless of tumor stage and a higher tumor histologic grade. Conclusion: Altogether, our study suggests that CDCA3 and UBE2C may be valuable biomarkers for predicting the outcome of patients with advanced serous ovarian cancer.